The documents listed below are incorporated herein by reference:
G. Bishop, and G. Welch, “An Introduction to the Kalman Filter”, SIGGRAPH 2001, Course 8.
K. Jacobsson, H. Hjalmarsson, N. Möller and K H Johansson, “Estimation of RTT and bandwidth for congestion control applications in communication networks”, in IEEE Conference on Decision and Control (CDC) 2004 Proceedings, Paradise Island, Bahamas, 2004.
M. Jain and C. Dovrolis, “Pathload: a measurement tool for end-to-end available bandwidth”. In Proc. of Passive and Active Measurement workshop (PAM), 2002.
M. Jain and C. Dovrolis, “End-to-end Available Bandwidth: Measurement Methodology, Dynamics, and Relation with TCP Throughput”. In Proc. of ACM Sigcomm, 2002.
S. Keshav, A control-theoretic approach to flow control. In Proceedings of ACM SIGCOMM'91, pages 3-15, Zurich, Switzerland, September 1991.
M. Kim and B. Noble, “SANE: stable agile network estimation”, University of Michigan Department of Electrical Engineering and Computer Science. CSE-TR-432-00. 2000.
M. Kim and B. Noble, “Mobile network estimation”, Mobile Computing and Networking (ACM MOBICOM), Rome, Italy, 2001.
B. Melander, M. Björkman, and P. Gunningberg, “A new end-to-end probing and analysis method for estimating bandwidth bottlenecks”, Proceedings of IEEE Globecomm '00, San Francisco, USA, November 2000.
A. Pásztor and D. Veitch, “The packet size dependence of packet-pair like methods”, in Proceedings, Tenth International Workshop on Quality of Service (IWQoS 2002), Miami Beach, USA, May 2002.
V. Ribeiro, R. Riedi, R. Baraniuk, J, Navratil, L. Cottrell, “pathChirp: Efficient Available Bandwidth Estimation for Network Paths”, in Proc. of Passive and Active Measurement workshop (PAM), 2003.
A. Shriram, M. Murray, Y. Hyun, N. Brownlee, A. Broido, M. Fomenkov and K C Claffy, “Comparison of Public End-to-End Bandwidth Estimation Tools on High-Speed Links”, in Passive and Active Measurement workshop (PAM), 2005.
Nowak, R. D. and Coates, M. J., “Network tomography using closely spaced unicast packets”, U.S. Pat. No. 6,839,754 (2005).
M. J. Coates and R. D. Nowak. “Sequential Monte Carlo inference of internal delays in nonstationary data networks”, IEEE Transactions on Signal Processing, 50(2):366-376, February 2002.
S. Ekelin and M. Nilsson, “Continuous monitoring of available bandwidth over a network path”, 2nd Swedish National Computer Networking Workshop, Karlstad, Sweden, Nov. 23-24, 2004.
Prasad, R., Murray, M., Dovrolis, C., Claffy, K C.: Bandwidth Estimation: Metrics, Measurement Techniques, and Tools. In: IEEE Network Magazine (2003).
The capability of measuring available bandwidth end-to-end over a path in a data network is useful in several contexts, including SLA (Service Level Agreement) verification, network monitoring and server selection. Passive monitoring of available bandwidth of an end-to-end network path is possible in principle, provided all of the network nodes in the path can be accessed. However, this is typically not possible, and estimation of available end-to-end bandwidth is typically done by active probing of the network path. The available bandwidth can be estimated by injecting probe traffic into the network, and then analyzing the observed effects of cross traffic on the probes. This kind of active measurement requires access to the sender and receiver hosts (nodes) only, and does not require access to any intermediate nodes in the path between the sender and receiver.
Conventional approaches to active probing require the injection of probe packet traffic into the path of interest at a rate that is sufficient transiently to use all available bandwidth and cause congestion. If only a small number of probe packets are used, then the induced transient congestion can be absorbed by buffer queues in the nodes. Accordingly, no packet loss is caused, but rather only a small path delay increase of a few packets. The desired measure of the available bandwidth is determined based on the delay increase. Probe packets can be sent in pairs or in trains, at various probe packet rates. The probe packet rate where the path delay begins increasing corresponds to the point of congestion, and thus is indicative of the available bandwidth. Probe packets can also be sent such that the temporal separation between probe packets within a given train varies, so each train can cover a range of probe rates.
Conventional solutions such as those mentioned above either do not produce real time estimates of available bandwidth, or do not produce sufficiently accurate estimates of available bandwidth, or both. These solutions also tend to require either significant data processing resources, or significant memory resources, or both.
It is therefore desirable to provide for an active probing solution that can estimate the available bandwidth of a path in a data network without the aforementioned difficulties of conventional solutions.